Data and AI Outlook

Unlocking the power of GenAI. Prioritizing holistic data for seamless collaboration and innovation will be key in 2024

Navigating Tomorrow - Forward Focus
With GenAI taking center stage in 2023 and being recognized as among the top priorities for many IT departments in 2024, the need to provide holistic/high-quality datasets to business analysts and machine learning communities for experimentation and analysis has become a top priority.

As we navigate through 2024, a seismic shift in data management practices is becoming increasingly evident. Organizations are adopting cloud data platforms at an unprecedented rate. Modern data platforms have become the mainstay, while Hyperscalers and ISVs continue to evolve platform architectures to make data management simpler, while deploying data products faster than ever.

1.pngOutlook #1 - Modernization and Cloud Data Platforms

Cloud Data Platform architectures have taken giant strides in simplifying how these platforms store, manage, and share data across various SaaS services for Data Engineering, Warehousing, Analytics and AI. Simplification and seamless integration of SaaS services needed to ingest, secure, analyze and experiment with enterprise datasets is trending. Co-pilots for these services are driving significant efficiencies in the process of development of data pipelines, analytical reports, and ML models.

By now, the benefits of adopting modern data platforms have been proven. Organizations that have made the transition have experienced significant improvements in their performance KPIs, while a reduction in data latency, improved data quality, and ease of democratization are a common theme across organizations that have made the transition.

Expect the trend of cloud data platform adoption to grow in 2024.   

2.pngOutlook #2 - Evolution of the Data Mesh

For the last few years, there has been much talk about the data mesh. Data management organizations have been deliberating on how to balance the recommendations around fundamental principles of the data mesh with the practicality of implementing these within their organizations.

‘Data mesh light’ has emerged as a pragmatic approach to adopt the principles of the data mesh while balancing it with the complexities of change management required to implement the original principles. Organizations have taken the approach of allowing step-by-step integration that aligns with the pace and complexity.

The concept of infusing product thinking into data assets and deploying data products has emerged as a clear winner and many organizations have changed their thinking to orient around treating their data assets as products.

Expect organizations to continue to iterate on approaches that are pragmatic to implement the data mesh.     

3.pngOutlook #3 - Data for Net Zero

Presently in many countries, sustainability reporting standards have several data-intensive disclosures covering greenhouse gases, energy, waste, water, recycling, and social metrics. In addition, customer demographics and buying habits are changing. People are cognizant of circular economies and are making buying decisions based on the sustainability of the products they buy.

This trend has led to a challenge for many data organizations as they strive to capture the data required to meet their net-zero objectives. In recent years, this trend has led many organizations to undertake endeavors to define sustainability programs with a focus on data. Several of them have invested in sustainability data hubs to support their needs around sustainability.

With the regulatory reporting landscape becoming more stringent, and mandates around ESG metrics, expect investments in sustainability data hubs to increase throughout this year.

4.pngOutlook #4 - Data Sharing

Data sharing beyond organizational boundaries is increasingly common across various industries, fostering collaboration, innovation, and efficiency. With the emergence of health information exchanges, open banking standards, supply chain collaboration, smart grids, government cross-agency collaboration, intelligent transportation systems etc., the need for data sharing has unprecedented demand.

In response to this demand, modern data platforms offer packaged functionality to share data securely across organizations. Many of these platforms offer secure data sharing with no actual data being copied or transferred between accounts. In addition, the development of interoperability standards, collaboration tools, and advanced integration capabilities have led many organizations to take a second look at how they share and monetize their data assets.

Expect data sharing to be a core requirement for data platform implementations and this trend to grow in 2024. 

5.pngOutlook #5 - Proliferation of Co-Pilots

In response to the recent advancements in Gen AI technologies, Hyperscalers and ISVs have added Gen AI based co-pilots to their suite of services to drive innovation, improve productivity, and align data systems. Nearly every aspect of data management and consumption will likely see the impact of these co-pilots.

As it relates to data management organizations, the functions of data integration, governance, access management, analytics and insights, AI explainability and model deployment, and serving will directly benefit from co-pilots. In addition, tools for synthetic data generation, co-pilots for quality engineering and co-pilots for SDLC will further enhance productivity.

Expect extensive evaluation of co-pilots and the integration of selected co-pilots into data management activities in 2024.


Prithvi Krishnappa
Prithvi Krishnappa
Global Head, Data and AI, Sogeti